went through the Robinhood MLE loop for a role on their personalization/recommendations team. here's the detailed breakdown.
team context: they're building features that personalize what users see in the app: what news, what stocks, what options flows to surface. the team is smaller than I expected, maybe 15-20 MLE headcount for the entire ML function when I talked to them.
rounds:
1. recruiter + hiring manager intro (combined, 45 min): first half was typical recruiter stuff. second half was the HM, who went deep on my recsys background fast. they wanted to understand not just what models I'd built but what my contribution was specifically. "what would have been different if you hadn't been on that project" kind of question. I liked that.
2. ML system design (60 min): design a feed ranking system. classic, but Robinhood's twist: you have to think about regulatory constraints on financial content. you can't just maximize engagement if the most engaging thing is a meme stock at 3am. they wanted to see that I understood the difference between optimizing a pure engagement objective and a more constrained real-world one.
3. coding (45 min): medium-level algorithm question. nothing ML-specific, just general coding. array manipulation, nothing on graphs or DP. they said explicitly that they test on general coding ability, not ML-specific coding, and that made sense.
4. ML concepts (45 min): this was genuinely technical. covered: bias-variance tradeoff (with a concrete scenario, not the textbook definition) feature engineering for sparse user behavior data how to handle cold start for new users who have no trading history online vs. batch learning tradeoffs in a latency-sensitive app
5. behavioral (30 min): two people on this call. standard questions but they probed hard on conflict stories. wanted specifics, not generalities.
overall impression: solid team, technically serious. the financial-domain awareness they expect is real. if you come in thinking it's the same as a Netflix/Spotify recsys loop, you'll miss the regulatory and risk angle. prep for that.